arXiv:2606.17118v1 Announce Type: cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE-MLLMs) offer remarkable performance but incur prohibitive GPU memory costs, making compression essential. Among PTQ methods, expert-level mixed-precision quantization has proven effective for MoE-LLMs, yet suffers notable degradation on MoE-MLLMs due to two overlooked biases in expert importance estimation. (1) At the cross-modal level, the numerical dominance of vision tokens causes expert selection frequency to be dominated by vision tokens, masking experts that are critical to the text
As Mixture-of-Experts (MoE) models and multimodal capabilities become central to large language models (LLMs), the challenge of their prodigious memory requirements is acutely felt, demanding novel compression techniques.
Efficient training and deployment of advanced multimodal AI models are critical for the continued scaling and democratization of AI, directly impacting the economic viability of these complex systems.
This research introduces solutions to key biases in MoE multimodal LLM quantization, potentially unlocking more effective compression and wider adoption of these powerful, yet memory-intensive, models.
- · AI developers
- · Cloud computing providers
- · Edge AI providers
- · Deep learning researchers
- · Companies without efficient AI compression strategies
- · Users with limited GPU resources
More memory-efficient MoE Multimodal LLMs become viable for a broader range of applications and hardware.
Increased accessibility of advanced multimodal AI could accelerate innovation across various industries dependent on visual and textual data processing.
The reduced computational cost may lead to a higher demand for specialized AI hardware, optimizing for these newly efficient models.
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Read at arXiv cs.AI